IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v58y2016icp46-54.html
   My bibliography  Save this article

DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status

Author

Listed:
  • Misiunas, Nicholas
  • Oztekin, Asil
  • Chen, Yao
  • Chandra, Kavitha

Abstract

The problem of effectively preprocessing a dataset containing a large number of performance metrics and an even larger number of records is crucial when utilizing an ANN. As such, this study proposes deploying DEA to preprocess the data to remove outliers and hence, preserve monotonicity as well as to reduce the size of the dataset used to train the ANN. The results of this novel data analytic approach, i.e. DEANN, proved that the accuracy of the ANN can be maintained while the size of the training dataset is significantly reduced. DEANN methodology is implemented via the problem of predicting the functional status of patients in organ transplant operations. The results yielded are very promising which validates the proposed method.

Suggested Citation

  • Misiunas, Nicholas & Oztekin, Asil & Chen, Yao & Chandra, Kavitha, 2016. "DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status," Omega, Elsevier, vol. 58(C), pages 46-54.
    • Handle: RePEc:eee:jomega:v:58:y:2016:i:c:p:46-54
    DOI: 10.1016/j.omega.2015.03.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048315000626
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2015.03.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Seiford, Lawrence M. & Zhu, Joe, 2003. "Context-dependent data envelopment analysis--Measuring attractiveness and progress," Omega, Elsevier, vol. 31(5), pages 397-408, October.
    2. Kazemi Matin, Reza & Kuosmanen, Timo, 2009. "Theory of integer-valued data envelopment analysis under alternative returns to scale axioms," Omega, Elsevier, vol. 37(5), pages 988-995, October.
    3. Zhuang, Zoe Y. & Churilov, Leonid & Burstein, Frada & Sikaris, Ken, 2009. "Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners," European Journal of Operational Research, Elsevier, vol. 195(3), pages 662-675, June.
    4. Joe Zhu, 2014. "Quantitative Models for Performance Evaluation and Benchmarking," International Series in Operations Research and Management Science, Springer, edition 3, number 978-3-319-06647-9, December.
    5. Olanrewaju, O.A. & Jimoh, A.A. & Kholopane, P.A., 2012. "Integrated IDA–ANN–DEA for assessment and optimization of energy consumption in industrial sectors," Energy, Elsevier, vol. 46(1), pages 629-635.
    6. Kuosmanen, Timo & Matin, Reza Kazemi, 2009. "Theory of integer-valued data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 192(2), pages 658-667, January.
    7. Cook, Wade D. & Zhu, Joe, 2006. "Rank order data in DEA: A general framework," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1021-1038, October.
    8. Hatami-Marbini, Adel & Emrouznejad, Ali & Tavana, Madjid, 2011. "A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making," European Journal of Operational Research, Elsevier, vol. 214(3), pages 457-472, November.
    9. Bergmeir, Christoph & Benítez, José M., 2012. "Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 46(i07).
    10. Joe Zhu, 2014. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 1, pages 1-9, Springer.
    11. Shouhong Wang, 1995. "The Unpredictability of Standard Back Propagation Neural Networks in Classification Applications," Management Science, INFORMS, vol. 41(3), pages 555-559, March.
    12. Nahra, Tammie A. & Mendez, David & Alexander, Jeffrey A., 2009. "Employing super-efficiency analysis as an alternative to DEA: An application in outpatient substance abuse treatment," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1097-1106, August.
    13. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    14. Chen, Yao, 2005. "Measuring super-efficiency in DEA in the presence of infeasibility," European Journal of Operational Research, Elsevier, vol. 161(2), pages 545-551, March.
    15. Cook, Wade D. & Tone, Kaoru & Zhu, Joe, 2014. "Data envelopment analysis: Prior to choosing a model," Omega, Elsevier, vol. 44(C), pages 1-4.
    16. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "Data envelopment analysis 1978–2010: A citation-based literature survey," Omega, Elsevier, vol. 41(1), pages 3-15.
    17. Samoilenko, Sergey & Osei-Bryson, Kweku-Muata, 2013. "Using Data Envelopment Analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system," Omega, Elsevier, vol. 41(1), pages 131-142.
    18. Zhu, Joe, 2001. "Super-efficiency and DEA sensitivity analysis," European Journal of Operational Research, Elsevier, vol. 129(2), pages 443-455, March.
    19. Samoilenko, Sergey & Osei-Bryson, Kweku-Muata, 2010. "Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks," European Journal of Operational Research, Elsevier, vol. 206(2), pages 479-487, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    2. Ya Chen & Wade D. Cook & Juan Du & Hanhui Hu & Joe Zhu, 2017. "Bounded and discrete data and Likert scales in data envelopment analysis: application to regional energy efficiency in China," Annals of Operations Research, Springer, vol. 255(1), pages 347-366, August.
    3. Liu, Jiawen & Gong, Yeming (Yale) & Zhu, Joe & Zhang, Jinlong, 2018. "A DEA-based approach for competitive environment analysis in global operations strategies," International Journal of Production Economics, Elsevier, vol. 203(C), pages 110-123.
    4. Monireh Jahani Sayyad Noveiri & Sohrab Kordrostami & Alireza Amirteimoori, 2022. "Performance analysis of sustainable supply networks with bounded, discrete, and joint factors," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(1), pages 238-270, January.
    5. González, Eduardo & Cárcaba, Ana & Ventura, Juan, 2015. "How car dealers adjust prices to reach the product efficiency frontier in the Spanish automobile market," Omega, Elsevier, vol. 51(C), pages 38-48.
    6. Gholam R. Amin & Osama El‐Temtamy & Samy Garas, 2022. "Audit Risk Evaluation Using Data Envelopment Analysis with Ordinal Data," Abacus, Accounting Foundation, University of Sydney, vol. 58(3), pages 589-602, September.
    7. Du, Juan & Chen, Chien-Ming & Chen, Yao & Cook, Wade D. & Zhu, Joe, 2012. "Additive super-efficiency in integer-valued data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 218(1), pages 186-192.
    8. Wang, Ke & Wei, Yi-Ming & Huang, Zhimin, 2016. "Potential gains from carbon emissions trading in China: A DEA based estimation on abatement cost savings," Omega, Elsevier, vol. 63(C), pages 48-59.
    9. Fabienne Miller & Justin Wang & Joe Zhu & Ya Chen & Jason Hockenberry, 2017. "Investigation of the Impact of the Massachusetts Health Care Reform on Hospital Costs and Quality of Care," Annals of Operations Research, Springer, vol. 250(1), pages 129-146, March.
    10. Sahoo, Biresh & Singh, Ramadhar & Mishra, Bineet & Sankaran, Krithiga, 2015. "Research Productivity in Management Schools of India: A Directional Benefit-of-Doubt Model Analysis," MPRA Paper 67046, University Library of Munich, Germany.
    11. Ramón, Nuria & Ruiz, José L. & Sirvent, Inmaculada, 2020. "Cross-benchmarking for performance evaluation: Looking across best practices of different peer groups using DEA," Omega, Elsevier, vol. 92(C).
    12. Kadziński, Miłosz & Labijak, Anna & Napieraj, Małgorzata, 2017. "Integrated framework for robustness analysis using ratio-based efficiency model with application to evaluation of Polish airports," Omega, Elsevier, vol. 67(C), pages 1-18.
    13. Telles, Eduardo Santos & Lacerda, Daniel Pacheco & Morandi, Maria Isabel Wolf Motta & Piran, Fabio Antonio Sartori, 2020. "Drum-buffer-rope in an engineering-to-order system: An analysis of an aerospace manufacturer using data envelopment analysis (DEA)," International Journal of Production Economics, Elsevier, vol. 222(C).
    14. Sahoo, Biresh K. & Singh, Ramadhar & Mishra, Bineet & Sankaran, Krithiga, 2017. "Research productivity in management schools of India during 1968-2015: A directional benefit-of-doubt model analysis," Omega, Elsevier, vol. 66(PA), pages 118-139.
    15. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "A survey of DEA applications," Omega, Elsevier, vol. 41(5), pages 893-902.
    16. Dyckhoff, Harald & Souren, Rainer, 2022. "Integrating multiple criteria decision analysis and production theory for performance evaluation: Framework and review," European Journal of Operational Research, Elsevier, vol. 297(3), pages 795-816.
    17. Chowdhury, Hedayet & Zelenyuk, Valentin, 2016. "Performance of hospital services in Ontario: DEA with truncated regression approach," Omega, Elsevier, vol. 63(C), pages 111-122.
    18. Afsharian, Mohsen & Bogetoft, Peter, 2020. "Identifying production units with outstanding performance," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1191-1194.
    19. Youchao Tan & Yang Zhang & Roohollah Khodaverdi, 2017. "Service performance evaluation using data envelopment analysis and balance scorecard approach: an application to automotive industry," Annals of Operations Research, Springer, vol. 248(1), pages 449-470, January.
    20. Hukom, Venticia & Nielsen, Rasmus & Asmild, Mette & Nielsen, Max, 2020. "Do Aquaculture Farmers Have an Incentive to Maintain Good Water Quality? The Case of Small-Scale Shrimp Farming in Indonesia," Ecological Economics, Elsevier, vol. 176(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jomega:v:58:y:2016:i:c:p:46-54. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.